i-choose u: Development of Decision Support System Using Skyline Technique (Preference Query Technique): The Case of Choosing Malaysian Higher Learning Institutions

This study focuses on Malaysian public universities marketing strategies which mainly used to attract international students who have the intention to enroll in higher learning institutions in Malaysia. The main objective of this study is to develop decision support system prototype (DSS) using preferences queries technique (skyline technique), in order to solve the issue of facing challenges that can be consequences of wrong selection of universities or colleges that had been made by students and may influence in their performance. This system aim to help international students to choose suitable college based on their criteria as well as to help them to make the right decision when they want to select one of the public universities in Malaysia. In this research; we used rapid application development (RAD) method. The DSS prototype (i-choose u) in this study constructed by using Java Server Pages (JSP) and MYSQL for database development, which are open sources software. The DSS prototype (i-choose u) suggests maximum five universities to international students that are most suitable to students based on student’s criteria

Hardware-conscious query processing for the many-core era

Die optimale Nutzung von moderner Hardware zur Beschleunigung von Datenbank-Anfragen ist keine triviale Aufgabe. Viele DBMS als auch DSMS der letzten Jahrzehnte basieren auf Sachverhalten, die heute kaum noch Gültigkeit besitzen. Ein Beispiel hierfür sind heutige Server-Systeme, deren Hauptspeichergröße im Bereich mehrerer Terabytes liegen kann und somit den Weg für Hauptspeicherdatenbanken geebnet haben. Einer der größeren letzten Hardware Trends geht hin zu Prozessoren mit einer hohen Anzahl von Kernen, den sogenannten Manycore CPUs. Diese erlauben hohe Parallelitätsgrade für Programme durch Multithreading sowie Vektorisierung (SIMD), was die Anforderungen an die Speicher-Bandbreite allerdings deutlich erhöht. Der sogenannte High-Bandwidth Memory (HBM) versucht diese Lücke zu schließen, kann aber ebenso wie Many-core CPUs jeglichen Performance-Vorteil negieren, wenn dieser leichtfertig eingesetzt wird. Diese Arbeit stellt die Many-core CPU-Architektur zusammen mit HBM vor, um Datenbank sowie Datenstrom-Anfragen zu beschleunigen. Es wird gezeigt, dass ein hardwarenahes Kostenmodell zusammen mit einem Kalibrierungsansatz die Performance verschiedener Anfrageoperatoren verlässlich vorhersagen kann. Dies ermöglicht sowohl eine adaptive Partitionierungs und Merge-Strategie für die Parallelisierung von Datenstrom-Anfragen als auch eine ideale Konfiguration von Join-Operationen auf einem DBMS. Nichtsdestotrotz ist nicht jede Operation und Anwendung für die Nutzung einer Many-core CPU und HBM geeignet. Datenstrom-Anfragen sind oft auch an niedrige Latenz und schnelle Antwortzeiten gebunden, welche von höherer Speicher-Bandbreite kaum profitieren können. Hinzu kommen üblicherweise niedrigere Taktraten durch die hohe Kernzahl der CPUs, sowie Nachteile für geteilte Datenstrukturen, wie das Herstellen von Cache-Kohärenz und das Synchronisieren von parallelen Thread-Zugriffen. Basierend auf den Ergebnissen dieser Arbeit lässt sich ableiten, welche parallelen Datenstrukturen sich für die Verwendung von HBM besonders eignen. Des Weiteren werden verschiedene Techniken zur Parallelisierung und Synchronisierung von Datenstrukturen vorgestellt, deren Effizienz anhand eines Mehrwege-Datenstrom-Joins demonstriert wird.Exploiting the opportunities given by modern hardware for accelerating query processing speed is no trivial task. Many DBMS and also DSMS from past decades are based on fundamentals that have changed over time, e.g., servers of today with terabytes of main memory capacity allow complete avoidance of spilling data to disk, which has prepared the ground some time ago for main memory databases. One of the recent trends in hardware are many-core processors with hundreds of logical cores on a single CPU, providing an intense degree of parallelism through multithreading as well as vectorized instructions (SIMD). Their demand for memory bandwidth has led to the further development of high-bandwidth memory (HBM) to overcome the memory wall. However, many-core CPUs as well as HBM have many pitfalls that can nullify any performance gain with ease. In this work, we explore the many-core architecture along with HBM for database and data stream query processing. We demonstrate that a hardware-conscious cost model with a calibration approach allows reliable performance prediction of various query operations. Based on that information, we can, therefore, come to an adaptive partitioning and merging strategy for stream query parallelization as well as finding an ideal configuration of parameters for one of the most common tasks in the history of DBMS, join processing. However, not all operations and applications can exploit a many-core processor or HBM, though. Stream queries optimized for low latency and quick individual responses usually do not benefit well from more bandwidth and suffer from penalties like low clock frequencies of many-core CPUs as well. Shared data structures between cores also lead to problems with cache coherence as well as high contention. Based on our insights, we give a rule of thumb which data structures are suitable to parallelize with focus on HBM usage. In addition, different parallelization schemas and synchronization techniques are evaluated, based on the example of a multiway stream join operation

Global Sensor Networks

The availability of cheap and smart wireless sensing devices provides unprecedented possibilities to monitor the physical world. On the technical side these devices introduce several original research problems, many of them related to the integration of the rampant technology proposals. Global Sensor Network (GSN) is a platform which provides a scalable infrastructure for integrating heterogeneous sensor network technologies using a small set of powerful abstractions. GSN supports the integration and discovery of sensor networks and sensor data, provides distributed querying, filtering, and combination of sensor data, and supports the dynamic adaption of the system configuration during operation through a declarative XML-based languag

Study and Application of Data Stream Processing System over High-speed Networks

传统的数据库管理系统适合于处理针对有限存储的数据集的一次性查询。然而，像网络监控、金融分析、制造业和传感器网络等现代的应用却需要针对连续而且无限的数据流进行持续查询。在这些应用中，数据不再以有限存储的数据集的形式存在，而是以多重、持续、快速、时变形式到达的数据流。在处理这些需要实时响应的大容量数据流时，传统数据处理基本结构的局限性更加突出。使用传统的数据库管理系统，简单地将到达的数据装入数据库中再进行操作是不可行的，因为它不能直接支持数据流处理中典型的持续查询，更重要的是，近似问题和适应性是数据流处理的两个关键因素，而传统的数据库管理系统关注的却是使用稳定的查询计划获得精确的结果。因此，现有的...Traditional database management systems are best equipped to run one-time queries over finite stored data sets. However, many modern applications such as network monitoring, financial analysis, manufacturing, and sensor networks require continuous queries over continuous unbounded streams of data. In these applications, data does not take the form of finite stored data sets, but rather arrives in ...学位：工程硕士院系专业：信息科学与技术学院自动化系_控制工程学号：X20043104

Infrastructure for data processing in large-scale interconnected sensor networks

With the price of wireless sensor technologies diminishing rapidly we can expect large numbers of autonomous sensor networks being deployed in the near future. These sensor networks will typically not remain isolated but the need of interconnecting them on the network level to enable integrated data processing will arise, thus realizing the vision of a global “Sensor Internet.” This requires a flexible middleware layer which abstracts from the underlying, heterogeneous sensor network technologies and supports fast and simple deployment and addition of new platforms, facilitates efficient distributed query processing and combination of sensor data, provides support for sensor mobility, and enables the dynamic adaption of the system configuration during runtime with minimal (zeroprogramming) effort. This paper describes the Global Sensor Networks (GSN) middleware which addresses these goals. We present GSN’s conceptual model, abstractions, and architecture, and demonstrate the efficiency of the implementation through experiments with typical high-load application profiles. The GSN implementation is available from http://globalsn.sourceforge.net/

A Heartbeat Mechanism and its Application in Gigascope.

Abstract Data stream management systems often rely on ordering properties of tuple attributes in order to implement non-blocking operators. However, query operators that work with multiple streams, such as stream merge or join, can often still block if one of the input stream is very slow or bursty. In principle, punctuation and heartbeat mechanisms have been proposed to unblock streaming operators. In practice, it is a challenge to incorporate such mechanisms into a highperformance stream management system that is operational in an industrial application. In this paper, we introduce a system for punctuation-carrying heartbeat generation that we developed for Gigascope, a high-performance streaming database for network monitoring, that is operationally used within AT&T's IP backbone. We show how heartbeats can be regularly generated by low-level nodes in query execution plans and propagated upward unblocking all streaming operators on its way. Additionally, our heartbeat mechanism can be used for other applications in distributed settings such as detecting node failures, performance monitoring, and query optimization. A performance evaluation using live data feeds shows that our system is capable of working at multiple Gigabit line speeds in a live, industrial deployment and can significantly decrease the query memory utilization

Towards consolidated presence

hauswirth2010aInternational audiencePresence management, i.e., the ability to automatically identify the status and availability of communication partners, is becoming an invaluable tool for collaboration in enterprise contexts. In this paper, we argue for efficient presence management by means of a holistic view of both physical context and virtual presence in online communication channels. We sketch the components for enabling presence as a service integrating both online information as well as physical sensors, discussing benefits, possible applications on top, and challenges of establishing such a service

The Global Sensor Networks middleware for efficient and flexible deployment and interconnection of sensor networks

The lack of standardization and the continuous inflow of novel sensor network technologies have made their deployment the main factor of manpower consumption, considerably complicate the interconnection of heterogeneous sensor networks, and make portable application development a challenging and time-consuming task. To address these problems we propose our Global Sensor Networks middleware which supports the rapid and simple deployment of a wide range of sensor network technologies, facilitates the flexible integration and discovery of sensor networks and sensor data, enables fast deployment and addition of new platforms, provides distributed querying, filtering, and combination of sensor data, and supports the dynamic adaption of the system configuration during operation. GSN offers virtual sensors as a simple and powerful abstraction which enables the user to declaratively specify XML-based deployment descriptors in combination with the possibility to integrate sensor network data through plain SQL queries over local and remote sensor data sources. The paper describes GSN's conceptual model and system architecture, and demonstrates the efficiency of the implementation through experiments with typical high-load application profiles. The GSN implementation is available from http://globalsn.sourceforge.net/